{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 共享单车的数据分析\n",
    "请在Capital Bikeshare （美国Washington, D.C.的一个共享单车公司）提供的自行车数据上\n",
    "进行回归分析。训练数据为2011年的数据，要求预测2012年每天的单车共享数量。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1  导入必要的包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#from sklearn import datasets, linear_model\n",
    "from sklearn.linear_model import LassoCV #lasso 回归\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import  RidgeCV #岭回归\n",
    "from sklearn.metrics import r2_score  #评价回归预测模型的性能\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 导入数据并进行数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"day.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由前五行数据看出, instant是数据编号与预测无关可以删去。\n",
    "               dteday是数据时间而且按天增长，与预测无关可以删去。\n",
    "               casual,registered 为预测值并且这个作业不使用，删去。\n",
    "               yr 是年，要求2012年的所有数据为测试值。训练值中yr全部为0，对预测没有任何作用，可以删去。\n",
    "               season,mnth,holiday,weekday,workingday,weathersit 是分类型特征\n",
    "               temp,atemp,hum,windspeed 是数值型特征,针对数值型特征归一化就是针对这几个变量。\n",
    "               cnt 是预测值，数值型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删去不用的特征及预测值\n",
    "data = data.drop(columns=['instant','casual','registered','dteday'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 数据非空检查"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season        0\n",
       "yr            0\n",
       "mnth          0\n",
       "holiday       0\n",
       "weekday       0\n",
       "workingday    0\n",
       "weathersit    0\n",
       "temp          0\n",
       "atemp         0\n",
       "hum           0\n",
       "windspeed     0\n",
       "cnt           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 判断数据有无空值\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出每个特征值或者预测值中都没有空值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 预测值与特征值分开"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#根据年份划分测试数据和训练数据\n",
    "trainData = data[data[\"yr\"]==0]\n",
    "testData  = data[data[\"yr\"]==1]\n",
    "\n",
    "trainData = trainData.drop(columns=['yr'])\n",
    "testData  = testData.drop(columns=['yr'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 特征工程\n",
    "首先分析相关性，这样可以找到强关联的特征关系。通过 PCA 或者删除其中之一进行特征处理。\n",
    "然后再进行单特征值分析，这样可以避免处理完了噪声数据以后如果关联性强又进行特征值删除的操作。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 数值型单变量数据特征处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " （1） 风速 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6f69f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#风速直方图\n",
    "fig=plt.figure(figsize=(17,5))\n",
    "plt.subplot(121)\n",
    "sns.distplot(trainData.windspeed.values, bins=30, kde=True)\n",
    "plt.title(\"Histogram of Windspeed\");\n",
    "plt.xlabel('Value of Windspeed', fontsize=12)\n",
    "plt.ylabel('Probability', fontsize=12)\n",
    "\n",
    "#风速散点图\n",
    "plt.subplot(122)\n",
    "plt.scatter(range(trainData.shape[0]), trainData[\"windspeed\"].values,color='orange')\n",
    "plt.title(\"Distribution of Windspeed\");\n",
    "plt.xlabel('Count of Windspeeds', fontsize=12)\n",
    "plt.ylabel('Value of Windspeed', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从直方图上面可以看出风速左边上升快，右侧下降相对较慢，不太满足正态分布。并且在0.4以上有个别噪声，应该去掉。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainData= trainData[trainData['windspeed']<=0.5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（2） 湿度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6f95b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#湿度直方图\n",
    "fig=plt.figure(figsize=(17,5))\n",
    "plt.subplot(121)\n",
    "sns.distplot(trainData.hum.values, bins=30, kde=True)\n",
    "plt.title(\" Histogram of humidity\");\n",
    "plt.xlabel('hum value', fontsize=12)\n",
    "plt.ylabel('Probability', fontsize=12)\n",
    "#湿度散点图\n",
    "plt.subplot(122)\n",
    "plt.scatter(range(trainData.shape[0]), trainData[\"hum\"].values,color='orange')\n",
    "plt.title(\"Distribution of humidity\");\n",
    "plt.xlabel('Count of Hum', fontsize=12)\n",
    "plt.ylabel('Value of Hum', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图可以看出 0.2一下存在个别噪声点，应该去除掉。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainData= trainData[trainData['hum']>0.2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（3） 温度 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcd115f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#温度直方图\n",
    "fig=plt.figure(figsize=(17,5))\n",
    "plt.subplot(121)\n",
    "sns.distplot(trainData.temp.values, bins=30, kde=True)\n",
    "plt.title(\" Histogram of temperature\");\n",
    "plt.xlabel('temp value', fontsize=12)\n",
    "plt.ylabel('Probability', fontsize=12)\n",
    "#温度散点图\n",
    "plt.subplot(122)\n",
    "plt.scatter(range(trainData.shape[0]), trainData[\"temp\"].values,color='orange')\n",
    "plt.title(\"Distribution of temp\");\n",
    "plt.xlabel('Count of Temperature', fontsize=12)\n",
    "plt.ylabel('Value of Temperature', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  4.2 分类型单变量数据特征处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（1） 季节"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd227048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe06d2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd093898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "sns.countplot(trainData.season)\n",
    "plt.xlabel('1:spring, 2:summer, 3:fall, 4:winter')\n",
    "plt.title('count of season in dataset')\n",
    "fig=plt.figure()\n",
    "sns.pairplot(trainData,x_vars='season',y_vars='cnt',size=4)\n",
    "plt.xlabel('1:spring, 2:summer, 3:fall, 4:winter')\n",
    "plt.ylabel('count of total rental bikes')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用单车数在四个季节都比较多"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（2） holiday 假期"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe06b7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd121e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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r73mxZBGvQRUeDOWc61DO0hZ/AbwBzAO+B6yTdFbUgcVhd2u+y0W8drd6l7lzUSmnb/gW4NNmtg4gnPz5S+DRKAOLg3eZOxe/clpLN7clnNB6YHNE8cTK5145F7+eBgeeJ+k84FVJj0j6vKQZBKOSn48twgilRDf7XvV3ZM7tv3q6jvjLosfvAH8WPt4CHBRZRDGqTqcYNqiqpPcqk5LPvXIuQj31Xl0aZyD9oSVfYEdTji/d+1LJOJ3UIJHJeOJxLgqJ/mYVDL5070slvVdfuvclfAca56KT6KSTrelmecYaH6fjXFQSnXR8wqdz8etp7lWPy1eY2a2VDyde2eo0N50/luvuX93epnPT+WMrvhC1c65DT71Xe7N8xT6hsSXPL1ZuZM6UMRx78BDWbd7FL1Zu5NJTj/HdIJyLSE+9V1+PM5D+kK1OM/3kI2kIV7uvyaSYfvKRXtNxLkK9jveXVEuwPcwYghUEATCzyyKMKxa+tIVzpQoFo7E13+8Ls/8E+CNgMvA0wRrFOysaRT/Jm3XZZZ7fDxc2c643wbbCzZ22FW6u+LbC5SSdY83sH4EGM1tIsI/VxysaRT/xCZ/OdWhsyTOr00YFsxat6pfN9lrD+/ckfYxgl4ajKxpFP/Euc+c6xDVurZyks0DSQcDXCHZmeI1wy999XVrqcsJnWj7j0yVPXKsulJN0njCzd83sGTMbZWYHE2y4t8+rrU4zd+ka5kwZw5pvncWcKWOYu3QNtd575RIolepm1YUK96mU03hxPzChU9l9lLet8IDW0JzjnR3NTL79mfaySaNG0NCcY2htVT9G5lz8ajNphtZk+O55H2/fHWVoTYbaTEx7mUv6CEE3+YHhujptDqCo63xfVp0Sd0wfx9WLVnWskTx9HNW+oI5LoFRKDK2tIp1OIcGHhtZE0mXeU03neOBzwDBK19bZCVxR0Sj6iRGsqVOc2avTqQ/ut+NcQrTtZQ6031daTyOSHwIekjTJzJZH8un9LFeAK+95keXrt7WXTRo1gjsvqevHqJzrP3EMDiwnla2T9FWCbvL24/eHEcm+tIVzHYLBgS3MWrSyvblh3vTxjBhcXdHEU07SeQj4DfA4sF+tWN7QnGPWZ45l8scObZ/wufSVt70h2SVSY2ueWYtWttf8g8GBK7lzRl1FL7XKeaesmV1XsU8cQAZl0kw7+UiuXryqdLO9CrfWO7cvyFZ3U/Ov8BCScnrg/1PS2X19Y0m1klZIeknSq5K+HpYfI+k5SWsl/TzcPRRJNeHzdeHrRxe91/Vh+RpJk/saS3d257rZbC+3X1XonCtLY0s3gwP7YRrE1QSJp0nSDkk7Je0o47xm4DNmdiIwDjhT0ikEo5lvM7PRwLsEM9gJ7981s2OB28LjkHQCMI2g+/5M4AeSKpJ6B9dkOOSAGpZecxpvfOdsll5zGoccUONzr1wiZavSzJs+vmRw4Lzp48lWeJttWQwzqiVlgf8HXEmwO+gfmVlO0iRgjplNlrQ0fLxcUgb4X2Ak8BUAM/tu+F7tx3X3eXV1dVZfX99rXI3NObY3tjB7ScfKgTdPHcvwbDVZTzwugfay96qsA8vZy1ySLpb0j+HzIySdXFYEUlrSKoIdQZcR7In+npm1zajcCBwWPj4M2AAQvv4+MKK4vItz9krBjNlLVpdcXs1espqCL23hEqptnE5K4X0EA2XLubz6ATAJ+Kvw+S7g++W8uZnlzWwcwRo8JwMf7eqw8L6rn856KC8haaakekn1W7ZsKSc8st0sbeG1HOeiU07S+YSZXQU0AZjZu0B1Xz7EzN4DngJOAYaFl08QJKNN4eONwBEA4esHAtuLy7s4p/gzFphZnZnVjRw5sqy4fGkL5+JX1no6YcOtAUgaCRR6O0nSSEnDwseDgM8CrwO/Bi4ID5tBMA4IgmUzZoSPLwCetKDB6WFgWti7dQwwGlhRRty98qUtnItfOdcR84AHgYMlfZsgIXytjPMOBRaGCSsF3Gtm/ynpNWCxpG8BK4G7wuPvAn4iaR1BDWcagJm9KulegnV8csBVZlaRPrza6jRzH1xTshvE3KVruPWicZV4e+dcF8rqvQpnnJ9O0L7yhJm9HnVge6Pc3qudTa3MvPuFD8y9WnDJRB+R7Fzf7X3vlaSUpFfM7Pdm9n0z+95ATzh90ba0RfHllS9t4Vy0ery8MrNCOKL4SDN7K66g4uJLWzgXv3Iakg8FXpX0hKSH225RBxaHXMFY/sZWhmWrkGBYtorlb2wlV+EtN5xzHcppSN5vd/qsrUoz8ajhXHnPiyUTPmsrPOzbOdeh16RjZk/HEUh/2N3aMeETaJ/wueCSiQxN+w6fzkUh0UNviyd8tnWZz39qnU/4dC5Cif52NbXk+fLk4z8w4bOpJe9TIZyLSKKvIfLdTPj0vcydi05PW9C8TBcTKwkGAJmZjY0sqpj4XubOxa+nb9fnYouin7RN+Cwekdw24dNHJDsXjZ62oPlDnIH0h2x1mpvOH8t193e06dx0/tiKrwnrnOvQ63VEuMTovxCshVMNpIEGMzsg4tgit7ulwKub3mP+xRM4YFAVO3a3svyNrQwffDBDahPd3OVcZMppvPgewYzvJUAdcAlwbJRBxSWTosvBgRnPN85Fpqyvl5mtA9LhSoA/Aj4dbVjxaC1Yl7tBtPo0COciU05NpzHcJmaVpH8G3gYGRxtWPLz3yrn4lVPT+ZvwuC8CDQRLh54XZVBx8eVKnYtfOUnnXDNrMrMdZvZ1M7uW/aQ73ZcrdS5+5SSdGV2Ufb7CcfSL2uo0c5cGy5Wu+dZZzJkyhrlL11DrXebORaanEcnTCbadOabT+jkHANu6Pmvf0tCc450dzUy+/Zn2skmjRvjgQOci1FOL6e8IGo0/BNxSVL4TWB1lUHGpSYk7po3j6sWrSrrMa3y5Uuci09uI5D8AkyQdApwUvvR60Q6d+7SWAixe8VbJbhCLV7zFZaeO6tvGXs65spUzInkqMJdgszwB/yJptpndF3FskcvWpJn35DpufXxte1kmJb54+uh+jMq5/Vs5A1K+BpxkZpuhfbO9x4F9Pun4hE/n4ldO71WqLeGEtpV53oA3qCrNHdM6bUEzbRyDfI1k5yJTTk3nMUlLgUXh84uAR6MLKT67W/NdtulceuoxvkaycxEpd4fP84BTCdp0njGzB6MObG+Uu8NnwYzjbni0ZMuZTEr817fPIuUDBJ3rq73f4RNA0k1m9oCZXWtmf29mD0q6ae/j63+Nzfkup0E0Nldkq3TnXBfKuYb48y7Kzqp0IP0hJbqcBuHDdJyLTk8jkq8E/hYYJal4MOBQ4LdRBxaHmqoUg6szJdsKD67OUFPl7TnORaWnb9fPgL8EHg7v224Tzezi3t5Y0hGSfi3pdUmvSro6LB8uaZmkteH9QWG5JM2TtE7SakkTit5rRnj8WkldzQXbI625Ap2bbqSg3DkXjW6Tjpm9b2Zvmtl0M/tD0W17d+d0kgO+ZGYfBU4BrpJ0AvAV4AkzGw08ET6H4JJtdHibCcyHIEkBNwKfAE4GbmxLVHurtWDc/bs3aQ6TTHOuwN2/e9MX8XIuQpGtVmVmbxPM3cLMdkp6HTgMOAf4VHjYQoKRzteF5Xdb0J32rKRhkg4Nj13WluwkLQPOpKMLf49lq9OcO/5wX5jduRjF0ngh6WhgPPAccEiYkNoS08HhYYcBG4pO2xiWdVfe+TNmSqqXVL9ly5ay4mpsyXPd/aWb7V13/2oaW7z3yrmoRJ50JA0B7geuMbMdPR3aRZn1UF5aYLbAzOrMrG7kyJFlxebLlToXv0iTjqQqgoTzUzN7ICx+J7xsIrxvm2KxkWAp1DaHA5t6KN9rvlypc/GL7E+6JAF3ESyFcWvRSw8TrEb4T+H9Q0XlX5S0mKDR+H0zezucgvGdosbjM4DrKxHjoEya+RdP4L3G1vYu82HZKgZlvE3HuahEeR3xpwSLur8saVVY9lWCZHOvpMuBt4Cp4WuPAGcD64BG4FIAM9su6ZvA8+Fx3+hDD1qPcoWuu8ZzhQKZ/WNOq3MDTllzr/Y15c69amzOsb2xhdlLOnqvbp46luHZarLeruNcX1Vm7tX+rGAwe0lp79XsJavxYTrORSfRSSdbk+6y9ypb4206zkUl0UmnsZveq0bvvXIuMolOOimJ2y4qXTnwtovG+Vo6zkUo0a2l1ekUtVWpklnmtVUpqn3VQOcik+ikszuX58p7XixZmH3SqBEsuGQiQzOeeJyLQqKTzuCaDIccUMPSa05rXyN5/lPrfBqEcxFK9LerqSXPlycf/4FxOk0teR+n41xEEn0NUTDrZpyOD9RxLiqJTjrZbmaZey3HuegkOun4OB3nShUKxq7mHAUL7yMYnp/opJOSuOXCE0vG6dxy4Yk+TsclUqFgbGto4YqF9Rx3w6NcsbCebQ0tFU88iZ7wmS8U2L6rhYaWfNFuEGmGD6kmnUp0PnYJtKs5xxUL6z8whOTOGXUMKa/Joay/1oluvGhsyTNr8aqux+nUetJxyZKt7mYuYoXXDE/0N8uXK3WuQ2NLNzveVnjN8EQnHd9W2LkO2ao086aPL2njnDd9PNmqytZ0Et2m44t4OVeqUDAaW/Nkq9M0tuTJVqVJlb/Ptrfp9CYtGFJTuq3wkJoMae+8ci4yib68qqpKs2rDuwwfXI0EwwdXs2rDu1RVuDrp3L4gri7zRNd0Gppz/NvT/83y9R2XYpNGjWDiUcMZWlvVj5E5F7/G1jyzFq1s781dvn4bsxat7EuXeVkSXdMZVJXmjumli3jdMX0cg7ym4xIori7zRNd0WnIFajIp5l88gQMGVbFjdytSUJ7xhbxcwrR1mRePW2vrMveaTgXtbMpx5T0vctwNj3LlPS+ys8nnXblk8i7zvVBul/muphy/WbuZSR/+UHtNZ/kbW/nk6IMZUpvoSqBLKO8yj1htVYqJRw3nyntebB+nc8e0cdRWJb4C6BIqlVL7pVQlL6lKPiOSd91H7G7Nc3U496ptEa+rF69id6uPSHbJFMfSFomu6fjcK+c6tI3TmbVoZXvNf9708YwYXN2XS6xeJbqm09DNIl4NvoiXS6DicTptNf9Zi1bSWOGaf2RJR9IPJW2W9EpR2XBJyyStDe8PCsslaZ6kdZJWS5pQdM6M8Pi1kmZUMsZsdZqbzh9b0lp/0/ljKz4uwbl9wf6wtMWPgTM7lX0FeMLMRgNPhM8BzgJGh7eZwHwIkhRwI/AJ4GTgxrZEVQmNLXle3fQe8y+ewH99+yzmXzyBVze9V/Gp/M7tC/b5pS3M7Blge6fic4CF4eOFwLlF5Xdb4FlgmKRDgcnAMjPbbmbvAsv4YCLbY4My6fbeq7ZxOhOPGs6gjNd0XPIMyqS4Y1qnEfrTxjGowhtPxt2mc4iZvQ0Q3h8clh8GbCg6bmNY1l15RezOddN7lfOajkue3bkCi1e8xZwpY1jzrbOYM2UMi1e8xe5coaKfM1C6abpqGrceyj/4BtJMgkszjjzyyLI+1HuvnOuQrU4z78l13Pr42vayTEp88fTRFf2cuGs674SXTYT3m8PyjcARRccdDmzqofwDzGyBmdWZWd3IkSPLCsZ7r5zrsM+36XTjYaCtB2oG8FBR+SVhL9YpwPvh5ddS4AxJB4UNyGeEZRVRnVKX17DVFRyT4Ny+Yp+feyVpEfAp4EPAOwS9UL8A7gWOBN4CpprZdkkCvkfQSNwIXGpm9eH7XAZ8NXzbb5vZj3r77HLnXuVyBVryBXIFY0hthl1NOTIpUZ1Okalw45lz+4I45l4lesInBIlndy7P4JoMDc05BmXSnnCc2zNlJZ1Ef7vy+QK7WnJs29WCGWzb1cKulhz5fGVb651zHRKddJpzBXKF0gSTKxRornAXoXOuQ+L7hptaC1z/wMslW9BkfXlk5yKT6JpOwWD2ktUlgwNnL1lNBLP5nXOhRCedbE03E9xqfBqEc1FJdNLxwYHOxS/RSceXtnAufoluSN7dkucXKzcyZ8oYjj14COs27+IXKzdy2anHMKQ20fnYucgkOumkJM6beDizl6wu6b1KyadBOBeVRCed2uo0cx9cU1LTmbt0DbdeNK6/Q3Nuv5XopNPYnOedHc1Mvv2ve0dqAAAHoklEQVSZ9rJJo0bQ2Jz3fa+ci0iiGy5SgpunljYkB5dX/R2Zc/uvZP85V7Dh3nfP+zhHDM+yYXtjsNGeJx3nIpPomk5tVZpfrn6bYdkqJBiWreKXq9+mtsLrhzjnOiS6ptPUmuf0jx5Ssq3wzVPH0tSaJ1ud6F+Nc5FJdE2nUOhm7pVPMncuMolOOj73yrn4JTrpxLUQtXOuQ6KTTlwLUTvnOiS6tTSVEiMGV3PnjLo9XYjaOddHiU46ECSeIeHmekN8kz3nIpfoyyvnXPw86TjnYuVJxzkXK086zrlYedJxzsXKk45zLlb75V7mkrYAf+jjaR8CtkYQTiUM5NhgYMfnse2ZPYltq5md2dtB+2XS2ROS6s2srr/j6MpAjg0Gdnwe256JMja/vHLOxcqTjnMuVp50Oizo7wB6MJBjg4Edn8e2ZyKLzdt0nHOx8pqOcy5WiUs6ks6UtEbSOklf6eL1Gkk/D19/TtLRAyi2ayW9Jmm1pCckHTVQYis67gJJJim2XplyYpN0Yfi7e1XSz+KKrZz4JB0p6deSVob/tmfHFNcPJW2W9Eo3r0vSvDDu1ZImVOSDzSwxNyANvAGMAqqBl4ATOh3zt8C/ho+nAT8fQLF9GsiGj68cSLGFxw0FngGeBeoGSmzAaGAlcFD4/OAB9n9uAXBl+PgE4M2YYjsNmAC80s3rZwOPEmzKdArwXCU+N2k1nZOBdWa23sxagMXAOZ2OOQdYGD6+DzhdimVz815jM7Nfm1lj+PRZ4PAY4iorttA3gX8GmmKKq9zYrgC+b2bvApjZ5gEWnwEHhI8PBDbFEZiZPQNs7+GQc4C7LfAsMEzSoXv7uUlLOocBG4qebwzLujzGzHLA+8CIARJbscsJ/grFodfYJI0HjjCz/4wppjbl/N6OA46T9FtJz0rqddRsBZUT3xzgYkkbgUeAv4sntF719f9kWZK2VF5XNZbO3XflHBOFsj9X0sVAHfBnkUZU9JFdlLXHJikF3AZ8PqZ4ipXze8sQXGJ9iqB2+BtJHzOz9yKODcqLbzrwYzO7RdIk4CdhfP29GVIk34Wk1XQ2AkcUPT+cD1Zl24+RlCGo7vZUBY0zNiR9FrgBmGJmzTHEVU5sQ4GPAU9JepPg+v/hmBqTy/03fcjMWs3sv4E1BEkoDuXEdzlwL4CZLQdqCeY+9bey/k/2WVwNagPhRvAXbz1wDB2NemM6HXMVpQ3J9w6g2MYTNEqOHmi/t07HP0V8Dcnl/N7OBBaGjz9EcMkwYgDF9yjw+fDxR8MvtmKK72i6b0j+C0obkldU5DPj+MEG0o2gRf6/wi/vDWHZNwhqDhD8lVkCrANWAKMGUGyPA+8Aq8LbwwMltk7HxpZ0yvy9CbgVeA14GZg2wP7PnQD8NkxIq4AzYoprEfA20EpQq7kc+ALwhaLf2/fDuF+u1L+pj0h2zsUqaW06zrl+5knHORcrTzrOuVh50nHOxcqTjnMuVp50EqC32cThMXMkfbmP77srvP9jSfd1c8xTlR4kKOlmSb8PZz4/KGlYH84dJulvi55/SlLcUzcSzZNOMvyYYIBcJMxsk5ldENX7d2EZ8DEzG0sw/uX6Ppw7jGAlgYoIR627PvCkkwDW+2ziNieENZP1kma1FYbr+LwS3q7pfJKko9tqUZIGSVoc1kJ+DgwqOm6+pPpwTZuvh2WnS3qw6Jg/l/RALz/PryyYjAs9zLbvJu5/Aj4saZWkm8OyIZLuC2tPP21bVUDSRElPS3pB0tK2Gdbh7+g7kp4Gru4pVteFOEdm+q3/bnQx3J3S0adzgN8BNQRTBbYBVcBEgtGog4EhwKvA+PCcXZ3fG7gW+GH4eCyQIxzJCgwP79MEo5bHEox6/T0wMnztZ8Bf9uHn+g/g4vDxHwOPhI+7jLvz74FgEuj7BIkrBSwHTg1/9t8VxXVR0c/1FPCD/v433VdvXjVMMDP7105Fv7RgEmmzpM3AIQRfwAfNrAEgrIV8kmBRrK6cBswL33+1pNVFr10oaSbBfKRDCRazWi3pJwRLO/wImARcUk78km4gSGo/DT9vE8GUA3qI++Eu3mqFmW0Mj1tFkJjeI5jEuiys+KQJpgy0+Xk5MboP8qTjihXPWs8T/P/YkwXMPjC3RtIxwJeBk8zsXUk/JpjnBvAjghpLE7DEOi6duiVpBvA54HQLqx+dD+lDvN393K+a2aRuzmnow/u7It6m43rzDHCupKykwcD/AX7Ty/F/DSDpYwSXUBCsjNcAvC/pEOCsthPCGsom4GsEjd6E598t6eTOHxAuwnUdwYTJxs6v9xL3ToKlOHqzBhgZrm+DpCpJY8o4z/XCk04CSFpE0FZxvKSNki4Py78g6Qs9nWtmLxIkghXAc8C/m1l3l1YA8wkaZlcD/xCeh5m9RHBJ9irwQ4JZ1cV+Cmwws9eKysZSeknT5nsEiWNZ2CD8r+HP88eSHukpbjPbBvw2bFy+uYv3bvu5W4ALgJsktc3+/pMefm5XJp9l7gYESd8DVprZXeHzA4C7zGxq/0bmKs2Tjut3kl4guPT6c4tvNUTXTzzpOOdi5W06zrlYedJxzsXKk45zLlaedJxzsfKk45yLlScd51ys/j8CZGBsEBfXxAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd36f6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "sns.countplot(trainData.holiday)\n",
    "plt.xlabel('1:holiday, 2:other')\n",
    "plt.title('count of holiday in dataset')\n",
    "fig=plt.figure()\n",
    "sns.pairplot(trainData,x_vars='holiday',y_vars='cnt',size=4)\n",
    "plt.xlabel('1:holiday, 2:other')\n",
    "plt.ylabel('total rental bikes')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假期数量是很少的，但是假期使用车辆次数比较多"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（3） weathersit 晴雨天"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd232828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd120390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd2bb588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "sns.countplot(trainData.weathersit)\n",
    "plt.xlabel('1:Clear 2:Mist 3:Scouther 4:HeavySnowOrRain')\n",
    "plt.ylabel('count of weathersit in dataset')\n",
    "fig=plt.figure()\n",
    "sns.pairplot(trainData, x_vars='weathersit',y_vars='cnt',size=4)\n",
    "plt.xlabel('1:Clear 2:Mist 3:Scouther 4:HeavySnowOrRain')\n",
    "plt.ylabel('total rental bikes')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "没有雨雪天，晴天、阴天占大多数样本，没有大雪大雨天。模型训练后可能对这种情况预测有偏差。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（4） workingday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xde74470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd13cc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdde2898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "sns.countplot(trainData.workingday)\n",
    "fig=plt.figure()\n",
    "sns.pairplot(trainData, x_vars='workingday',y_vars='cnt',size=4)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "非工作日 单车每日数量最大值比工作日的最大值高，每日6000辆次数 非工作日比工作日的数量多"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(5) weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd20b358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd276c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "sns.countplot(trainData.weekday)\n",
    "sns.pairplot(trainData, x_vars='weekday',y_vars='cnt',size=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 相关性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe769b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temp and atemp = 1.00\n",
      "season and mnth = 0.83\n",
      "atemp and cnt = 0.78\n",
      "temp and cnt = 0.77\n",
      "weathersit and hum = 0.64\n",
      "season and cnt = 0.54\n"
     ]
    }
   ],
   "source": [
    "#get the names of all the columns\n",
    "cols=trainData.columns \n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "data_corr = trainData.corr().abs()\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "# Mask unimportant features\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "plt.show()\n",
    "\n",
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "size = data_corr.shape[0]\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1) or (data_corr.iloc[i,j] < 0 and data_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通常认为>0.5的两两关联属于强关联，由下图可知道 atemp 与 temp 相关性非常大 0.99 远大于0.5,属于强关联，两个特征与预测值关联系数相同为0.63。删除 atemp。season 与 mnth 也强关联为0.83。season 与 cnt 关联系数较大，所以删除 mnth。 weathersit 与 hum 虽然属于强关联，但是关联系数只有0.59 且一个是数值型特征，一个是分类型特征，保留。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe5fbe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10237128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103d4400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10540a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xeb92208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe5fbc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot of only the highly correlated pairs\n",
    "for v,i,j in s_corr_list:\n",
    "    sns.pairplot(trainData, size=6, x_vars=cols[i],y_vars=cols[j] )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除不需要的特征值\n",
    "trainData = trainData.drop(columns=['atemp','mnth'])\n",
    "testData = testData.drop(columns=['atemp','mnth'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  5 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  5.1 数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 分开特征值和预测值\n",
    "trainData_y = trainData.cnt.values\n",
    "trainData_x = trainData.drop(columns=['cnt'])\n",
    "testData_y = testData.cnt.values\n",
    "testData_x = testData.drop(columns=['cnt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "Continuity_fields = ['temp', 'hum', 'windspeed']\n",
    "\n",
    "#数值型特征归一化以及分类型特征编码同时处理\n",
    "n_train_x= preprocessing.Normalizer()\n",
    "n_train_y= preprocessing.Normalizer()\n",
    "n_test_x= preprocessing.Normalizer()\n",
    "n_test_y= preprocessing.Normalizer()\n",
    "enc_train = preprocessing.OneHotEncoder()\n",
    "enc_test = preprocessing.OneHotEncoder() \n",
    "\n",
    "#分离离散值与连续值\n",
    "continuousTrainData_x = trainData_x.loc[:, Continuity_fields]\n",
    "continuousTestData_x = testData_x.loc[:, Continuity_fields]\n",
    "distributiveTrainData_x = trainData_x.drop(columns = Continuity_fields)\n",
    "distributiveTestData_x = testData_x.drop(columns = Continuity_fields)\n",
    "\n",
    "#归一化连续特征值\n",
    "normalConTrainData_x = n_train_x.fit_transform(continuousTrainData_x)\n",
    "normalConTestData_x = n_test_x.fit_transform(continuousTestData_x)\n",
    "\n",
    "#热编码离散特征值\n",
    "normalDesTrainData_x = enc_train.fit_transform(distributiveTrainData_x).toarray()\n",
    "normalDesTestData_x = enc_test.fit_transform(distributiveTestData_x).toarray()\n",
    "\n",
    "#组合特征值\n",
    "xTrain = np.concatenate((normalDesTrainData_x,np.array(normalConTrainData_x)),axis=1)\n",
    "xTest = np.concatenate((normalDesTestData_x,np.array(normalConTestData_x)),axis=1)\n",
    "\n",
    "#归一化预测值\n",
    "yTrain =n_train_y.fit_transform(trainData_y.reshape(1, -1)).reshape(-1,1)\n",
    "yTest = n_test_y.fit_transform(testData_y.reshape(1, -1)).reshape(-1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于2011年的数据与2012年的数据存在时间差异，所以不用统一转化法则归一化或独热编码测试值和训练值。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6、模型训练及评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  6.1 尝试缺省参数的线性回归"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（1） 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[639931397530.8513]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[639931397530.8481]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[639931397530.8474]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[639931397530.8342]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[287172497520.279]</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[287172497520.27783]</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[270594673578.30835]</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[50928473451.038734]</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>[0.082733154296875]</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>[0.0220794677734375]</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>[-0.0185089111328125]</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[-114475975979.1651]</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[-114475975979.16565]</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[-114475975979.1665]</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[-114475975979.16652]</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[-114475975979.16682]</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[-131053799921.13013]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[-350720000048.40594]</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[-2085445059961.7305]</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[-2085445059961.7354]</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[-2085445059961.7515]</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     coef  columns\n",
       "3     [639931397530.8513]        3\n",
       "1     [639931397530.8481]        1\n",
       "2     [639931397530.8474]        2\n",
       "0     [639931397530.8342]        0\n",
       "12     [287172497520.279]       12\n",
       "6    [287172497520.27783]        6\n",
       "5    [270594673578.30835]        5\n",
       "14   [50928473451.038734]       14\n",
       "18    [0.082733154296875]       18\n",
       "19   [0.0220794677734375]       19\n",
       "20  [-0.0185089111328125]       20\n",
       "11   [-114475975979.1651]       11\n",
       "8   [-114475975979.16565]        8\n",
       "7    [-114475975979.1665]        7\n",
       "10  [-114475975979.16652]       10\n",
       "9   [-114475975979.16682]        9\n",
       "4   [-131053799921.13013]        4\n",
       "13  [-350720000048.40594]       13\n",
       "15  [-2085445059961.7305]       15\n",
       "16  [-2085445059961.7354]       16\n",
       "17  [-2085445059961.7515]       17"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "# 模型\n",
    "lr =LinearRegression()\n",
    "# 模型训练\n",
    "lr.fit(xTrain,yTrain)\n",
    "# 模型预测\n",
    "y_train_pred_lr = lr.predict(xTrain)\n",
    "y_test_pred_lr = lr.predict(xTest)\n",
    "# 观察模型权系数\n",
    "fs = pd.DataFrame({\"columns\":list(range(xTrain.shape[1])), \"coef\":list((lr.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（2） 评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Train r2_score:', 0.8094376439043923)\n",
      "('Test r2_score:', 0.7050877341399804)\n"
     ]
    }
   ],
   "source": [
    "print(\"Train r2_score:\", r2_score(yTrain,y_train_pred_lr))\n",
    "print(\"Test r2_score:\", r2_score(yTest,y_test_pred_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DTO1m+sFNqUiSJAFecUySpGIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBWqrt+TlqSebMzvU/tb1tLGsSctSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYXyPGmpYPWeV+z5x9LQZE9akqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYXqNaQjYlRE3BkRd0fE/RHx5Wr65Ii4IyIWRcQVEfGa5pcrSdLwUU9P+gXg4MzcB2gDDouI/YGvAt/IzF2BPwOnNK9MSZKGn15DOmtWVk9bqlsCBwNXV9MvBo5uSoWSJA1Tdf2edESMAOYDbwQuBH4PPJWZq6tZlgA79fDe2cBsgIkTJ/a3XmlI8PefJdWjroFjmbkmM9uACcA0YPfuZuvhvXMysz0z21tbW/teqSRJw8xGje7OzKeAW4D9gW0iorMnPgH4Y2NLkyRpeKtndHdrRGxTPd4CeCewELgZeF8120nAtc0qUpKk4aieY9LjgYur49KbAVdm5s8i4rfA5RFxNvAb4HtNrFOSpGGn15DOzHuAqd1Mf4ja8WlJktQEXnFMkqRCGdKSJBWqrvOkJZXN866locmetCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgrVa0hHxOsj4uaIWBgR90fEx6vpHRGxNCIWVLf3NL9cSZKGj83rmGc18OnMvCsitgLmR8RN1WvfyMzzmleeJEnDV68hnZnLgGXV42cjYiGwU7MLkyRpuNuoY9IRMQmYCtxRTfpYRNwTEd+PiG17eM/siJgXEfNWrFjRr2IlSRpO6g7piBgN/AT4RGY+A3wL2AVoo9bT/lp378vMOZnZnpntra2tDShZkqThoa6QjogWagF9WWb+FCAzl2fmmsx8GfgOMK15ZUqSNPzUM7o7gO8BCzPz612mj+8y23uB+xpfniRJw1c9o7vfBpwA3BsRC6ppXwCOi4g2IIFHgA81pUJJkoapekZ33wZENy/9e+PLkSRJnbzimCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSrU5oNdgKTho6OjsfNJQ509aUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlOdJSw3k+b2SGsmetCRJhTKkJUkqlCEtSVKhDGlJkgrVa0hHxOsj4uaIWBgR90fEx6vpYyPipohYVN1v2/xyJUkaPurpSa8GPp2ZuwP7Ax+NiCnA54G5mbkrMLd6LkmSGqTXkM7MZZl5V/X4WWAhsBMwC7i4mu1i4OhmFSlJ0nC0UcekI2ISMBW4A9ghM5dBLciBcT28Z3ZEzIuIeStWrOhftZIkDSN1h3REjAZ+AnwiM5+p932ZOScz2zOzvbW1tS81SpI0LNUV0hHRQi2gL8vMn1aTl0fE+Or18cBjzSlRkqThqZ7R3QF8D1iYmV/v8tJ1wEnV45OAaxtfniRJw1c91+5+G3ACcG9ELKimfQH4CnBlRJwC/D/gr5tToiRJw1OvIZ2ZtwHRw8szG1uOJEnq5BXHJEkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhNh/sAiRpXR0djZ1P2lTZk5YkqVCGtCRJhTKkJUkqVK8hHRHfj4jHIuK+LtM6ImJpRCyobu9pbpmSJA0/9fSkLwIO62b6NzKzrbr9e2PLkiRJvYZ0Zt4KPDkAtUiSpC76cwrWxyLiRGAe8OnM/HN3M0XEbGA2wMSJE/uxOqk+nr4jaajo68CxbwG7AG3AMuBrPc2YmXMysz0z21tbW/u4OkmShp8+hXRmLs/MNZn5MvAdYFpjy5IkSX0K6YgY3+Xpe4H7eppXkiT1Ta/HpCPix8AMYPuIWAKcCcyIiDYggUeADzWxRkmShqVeQzozj+tm8veaUIskSerCK45JklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEJtPtgFSKXr6BjsCiQNV/akJUkqlCEtSVKhDGlJkgplSEuSVKheQzoivh8Rj0XEfV2mjY2ImyJiUXW/bXPLlCRp+KmnJ30RcNg60z4PzM3MXYG51XNJktRAvYZ0Zt4KPLnO5FnAxdXji4GjG1yXJEnDXl/Pk94hM5cBZOayiBjX04wRMRuYDTBx4sQ+rk59sTHn9w7Hc4GH4zZL2rQ0feBYZs7JzPbMbG9tbW326iRJGjL6GtLLI2I8QHX/WONKkiRJ0PeQvg44qXp8EnBtY8qRJEmd6jkF68fAr4A3RcSSiDgF+ArwrohYBLyrei5Jkhqo14FjmXlcDy/NbHAtkiSpC684JklSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCtXX35OWpEFX72+CN+O31f09cg0Ee9KSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVCjPk5Y05HlOszZV9qQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCeJ61Nhue6Shpu7ElLklQoQ1qSpEIZ0pIkFcqQliSpUP0aOBYRjwDPAmuA1ZnZ3oiiJElSY0Z3H5SZjzdgOZIkqQt3d0uSVKj+9qQTuDEiEvh2Zs5Zd4aImA3MBpg4cWI/V6fBVu+5yo2eT5KGo/72pN+WmfsC7wY+GhEHrjtDZs7JzPbMbG9tbe3n6iRJGj76FdKZ+cfq/jHgGmBaI4qSJEn9COmI2DIitup8DBwC3NeowiRJGu76c0x6B+CaiOhczo8y8z8aUpUkSep7SGfmQ8A+DaxFkiR14SlYkiQVypCWJKlQ/p60msLzn6Uarxmg/rAnLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmF2qTPk96Y8wo9B1FSI/k/RQPBnrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFWqTPk96MDXjHG1/d1aS1JU9aUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkq1LA5T3owzy32vGZJvdkUrr3QjBoH06ZwbQp70pIkFcqQliSpUIa0JEmFMqQlSSpUv0I6Ig6LiAcjYnFEfL5RRUmSpH6EdESMAC4E3g1MAY6LiCmNKkySpOGuPz3pacDizHwoM18ELgdmNaYsSZIUmdm3N0a8DzgsM0+tnp8AvDUzP7bOfLOB2dXTNwEP9r3cPtseeHwQ1lsi26LGdniFbfEK26LGdnhFs9riDZnZ2ttM/bmYSXQzbb3Ez8w5wJx+rKffImJeZrYPZg2lsC1qbIdX2BavsC1qbIdXDHZb9Gd39xLg9V2eTwD+2L9yJElSp/6E9H8Bu0bE5Ih4DXAscF1jypIkSX3e3Z2ZqyPiY8DPgRHA9zPz/oZV1liDuru9MLZFje3wCtviFbZFje3wisE9XNvXgWOSJKm5vOKYJEmFMqQlSSrUkAnpiBgbETdFxKLqftse5jupmmdRRJzUZfp/RMTdEXF/RPxrdUW1TVJ/2iIiXhsR10fEA1VbfGVgq2+cBvxNnBMRf4iIlQNXdWP1duneiBgZEVdUr98REZO6vHZGNf3BiDh0IOtutL62Q0RsFxE3R8TKiLhgoOtuhn60xbsiYn5E3FvdHzzQtTdaP9piWkQsqG53R8R7m1ZkZg6JG/BPwOerx58HvtrNPGOBh6r7bavH21avbV3dB/AT4NjB3qbBaAvgtcBB1TyvAX4JvHuwt2mQ/ib2B8YDKwd7W/q4/SOA3wM7V5/l3cCUdeb5CPCv1eNjgSuqx1Oq+UcCk6vljBjsbRqEdtgSeDvwYeCCwd6WQW6LqcCO1eM9gaWDvT2D2BavBTavHo8HHut83ujbkOlJU7sk6cXV44uBo7uZ51Dgpsx8MjP/DNwEHAaQmc9U82xO7QPblEfU9bktMvO5zLwZIGuXe72L2jnwm6L+/k38OjOXDUilzVHPpXu7ttHVwMyIiGr65Zn5QmY+DCyulrcp6nM7ZOaqzLwNeH7gym2q/rTFbzKz81oY9wOjImLkgFTdHP1pi+cyc3U1fRRNzIuhFNI7dP5Dre7HdTPPTsAfujxfUk0DICJ+Tu0b0bPUPpBNVb/bAiAitgGOBOY2qc5ma0g7bMLq2ba181T/dJ4GtqvzvZuK/rTDUNOotvgr4DeZ+UKT6hwI/WqLiHhrRNwP3At8uEtoN1R/Lgs64CLi/wCv6+alL9a7iG6mrf0GlJmHRsQo4DLgYGq9qiI1uy0iYnPgx8D5mfnQxlc4MJrdDpu4eratp3mGUrv0px2Gmn63RUTsAXwVOKSBdQ2GfrVFZt4B7BERuwMXR8QNmdnwPS6bVEhn5jt7ei0ilkfE+MxcFhGdxwjWtQSY0eX5BOCWddbxfERcR203R7EhPQBtMQdYlJn/qwHlNs1A/E1swuq5dG/nPEuqL2ZjgCfrfO+moj/tMNT0qy0iYgJwDXBiZv6++eU2VUP+LjJzYUSsonacfl6jixxKu7uvAzpH5p4EXNvNPD8HDomIbauRvocAP4+I0dU/8c4e5HuABwag5mbpc1sARMTZ1P4YPzEAtTZTv9phCKjn0r1d2+h9wC+yNhrmOuDYanTrZGBX4M4BqrvR+tMOQ02f26I6/HU9cEZm3j5gFTdPf9picpUVRMQbqP3C4yNNqXKwR9g16kbtOMFcYFF1P7aa3g58t8t8J1MbBLMY+B/VtB2qD+weagMi/pkmjdTbBNpiArXdOQuBBdXt1MHepoFuh2r6P1H7Jv1ydd8x2NvUhzZ4D/A7aqNYv1hNOws4qno8Criq2vY7gZ27vPeL1fseZBMd4d+gdniEWu9pZfV3MGWg6y+hLYC/A1Z1+b+wABg32NszSG1xQpUVC6gNrj26WTV6WVBJkgo1lHZ3S5I0pBjSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIK9f8BAKr0WUW5pmQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe7730f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(yTest - y_test_pred_lr,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Histogram of Residuals\") \n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "残差分布比较理想，残差分布和高斯分布比较匹配，不过在0.02以上还有噪声 -0.03以下也有噪声"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10528f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#还可以观察预测值与真值的散点图\n",
    "plt.figure(figsize=(7, 5))\n",
    "plt.scatter(yTest, y_test_pred_lr)\n",
    "plt.plot([-0.5, 0.5], [-0.5, 0.5], '--k')   #数据已经归一，0.5倍标准差即可\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  6.2 岭回归"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（1） 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.707115927100043\n",
      "The r2 score of RidgeCV on train is 0.8129524461164463\n"
     ]
    }
   ],
   "source": [
    "#岭回归／L2正则\n",
    "#class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, \n",
    "#                                  normalize=False, scoring=None, cv=None, gcv_mode=None, \n",
    "#                                  store_cv_values=False)\n",
    "\n",
    "\n",
    "#设置超参数（正则参数）范围\n",
    "alphas = [ 0.001,0.005,0.01,0.02,0.03, 0.04,0.05,0.06,0.07,0.08,0.09,0.1,0.15,0.2]\n",
    "#n_alphas = 20\n",
    "#alphas = np.logspace(-5,2,n_alphas)\n",
    "\n",
    "#生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#模型训练\n",
    "ridge.fit(xTrain, yTrain)    \n",
    "\n",
    "#预测\n",
    "y_test_pred_ridge = ridge.predict(xTest) \n",
    "y_train_pred_ridge = ridge.predict(xTrain)\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print 'The r2 score of RidgeCV on test is', r2_score(yTest, y_test_pred_ridge)\n",
    "print 'The r2 score of RidgeCV on train is', r2_score(yTrain, y_train_pred_ridge)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(2) 评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe611940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('best alpha is:', 0.04)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[639931397530.8513]</td>\n",
       "      <td>[0.007180167919224847]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[639931397530.8481]</td>\n",
       "      <td>[0.0021253246144553456]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[639931397530.8474]</td>\n",
       "      <td>[0.0024781905960632317]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[639931397530.8342]</td>\n",
       "      <td>[-0.011783683129745481]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[287172497520.279]</td>\n",
       "      <td>[0.0017179804979826074]</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[287172497520.27783]</td>\n",
       "      <td>[-0.000337792614597654]</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[270594673578.30835]</td>\n",
       "      <td>[-0.0020268148041210354]</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[50928473451.038734]</td>\n",
       "      <td>[0.0006466269207348052]</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>[0.082733154296875]</td>\n",
       "      <td>[0.07591525042814762]</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>[0.0220794677734375]</td>\n",
       "      <td>[0.013604860893836501]</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>[-0.0185089111328125]</td>\n",
       "      <td>[-0.02220821146650221]</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[-114475975979.1651]</td>\n",
       "      <td>[0.00022442207894068922]</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[-114475975979.16565]</td>\n",
       "      <td>[-0.00021932587930195702]</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[-114475975979.1665]</td>\n",
       "      <td>[-0.00038227704726501166]</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[-114475975979.16652]</td>\n",
       "      <td>[-0.0003336223407074401]</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[-114475975979.16682]</td>\n",
       "      <td>[-0.000669384695051789]</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[-131053799921.13013]</td>\n",
       "      <td>[0.0020268148041209244]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[-350720000048.40594]</td>\n",
       "      <td>[-0.0006466269207354713]</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[-2085445059961.7305]</td>\n",
       "      <td>[0.00809900633159899]</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[-2085445059961.7354]</td>\n",
       "      <td>[0.0048531514792076735]</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[-2085445059961.7515]</td>\n",
       "      <td>[-0.012952157810806941]</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  coef_lr                 coef_ridge  columns\n",
       "3     [639931397530.8513]     [0.007180167919224847]        3\n",
       "1     [639931397530.8481]    [0.0021253246144553456]        1\n",
       "2     [639931397530.8474]    [0.0024781905960632317]        2\n",
       "0     [639931397530.8342]    [-0.011783683129745481]        0\n",
       "12     [287172497520.279]    [0.0017179804979826074]       12\n",
       "6    [287172497520.27783]    [-0.000337792614597654]        6\n",
       "5    [270594673578.30835]   [-0.0020268148041210354]        5\n",
       "14   [50928473451.038734]    [0.0006466269207348052]       14\n",
       "18    [0.082733154296875]      [0.07591525042814762]       18\n",
       "19   [0.0220794677734375]     [0.013604860893836501]       19\n",
       "20  [-0.0185089111328125]     [-0.02220821146650221]       20\n",
       "11   [-114475975979.1651]   [0.00022442207894068922]       11\n",
       "8   [-114475975979.16565]  [-0.00021932587930195702]        8\n",
       "7    [-114475975979.1665]  [-0.00038227704726501166]        7\n",
       "10  [-114475975979.16652]   [-0.0003336223407074401]       10\n",
       "9   [-114475975979.16682]    [-0.000669384695051789]        9\n",
       "4   [-131053799921.13013]    [0.0020268148041209244]        4\n",
       "13  [-350720000048.40594]   [-0.0006466269207354713]       13\n",
       "15  [-2085445059961.7305]      [0.00809900633159899]       15\n",
       "16  [-2085445059961.7354]    [0.0048531514792076735]       16\n",
       "17  [-2085445059961.7515]    [-0.012952157810806941]       17"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "#这是为了标出最佳参数的位置，不是必须\n",
    "#plt.plot(np.log10(ridge.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('best alpha is:', ridge.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(range(xTrain.shape[1])), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过不断尝试优化，找到 alpha 最优解0.04"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xddebfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(yTest - y_test_pred_ridge,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Histogram of Residuals\") \n",
    "ax.legend(loc='best');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe6050f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#还可以观察预测值与真值的散点图\n",
    "plt.figure(figsize=(7, 5))\n",
    "plt.scatter(yTest, y_test_pred_ridge)\n",
    "plt.plot([-0.5, 0.5], [-0.5, 0.5], '--k')   #数据已经归一，0.5倍标准差即可\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  6.3 Lasso"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "（1） 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Lasso／L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "\n",
    "#设置超参数搜索范围\n",
    "#alphas = [ 0.01*index,0.1*index,index,2*index,3*index, 5*index,7*index,10*index,100*index]\n",
    "#生成一个LassoCV实例\n",
    "lasso = LassoCV()  \n",
    "\n",
    "#训练（内含CV）\n",
    "lasso.fit(xTrain,yTrain)  \n",
    "\n",
    "#测试\n",
    "y_test_pred_lasso = lasso.predict(xTest) \n",
    "y_train_pred_lasso = lasso.predict(xTrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(2) 评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.7021407546903575\n",
      "The r2 score of LassoCV on train is 0.8113367070985658\n"
     ]
    }
   ],
   "source": [
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print 'The r2 score of LassoCV on test is', r2_score(yTest, y_test_pred_lasso)\n",
    "print 'The r2 score of LassoCV on train is', r2_score(yTrain, y_train_pred_lasso)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(3)超参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd2ae4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 3.687752981242293e-05)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lasso</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.566514e-03</td>\n",
       "      <td>[639931397530.8513]</td>\n",
       "      <td>[0.007180167919224847]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.618593e-04</td>\n",
       "      <td>[639931397530.8481]</td>\n",
       "      <td>[0.0021253246144553456]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>[639931397530.8474]</td>\n",
       "      <td>[0.0024781905960632317]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.484594e-02</td>\n",
       "      <td>[639931397530.8342]</td>\n",
       "      <td>[-0.011783683129745481]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5.481652e-04</td>\n",
       "      <td>[287172497520.279]</td>\n",
       "      <td>[0.0017179804979826074]</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-8.586456e-04</td>\n",
       "      <td>[287172497520.27783]</td>\n",
       "      <td>[-0.000337792614597654]</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-8.384169e-18</td>\n",
       "      <td>[270594673578.30835]</td>\n",
       "      <td>[-0.0020268148041210354]</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>[50928473451.038734]</td>\n",
       "      <td>[0.0006466269207348052]</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.478781e-02</td>\n",
       "      <td>[0.082733154296875]</td>\n",
       "      <td>[0.07591525042814762]</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>[0.0220794677734375]</td>\n",
       "      <td>[0.013604860893836501]</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-2.413996e-02</td>\n",
       "      <td>[-0.0185089111328125]</td>\n",
       "      <td>[-0.02220821146650221]</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.418903e-04</td>\n",
       "      <td>[-114475975979.1651]</td>\n",
       "      <td>[0.00022442207894068922]</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>[-114475975979.16565]</td>\n",
       "      <td>[-0.00021932587930195702]</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>[-114475975979.1665]</td>\n",
       "      <td>[-0.00038227704726501166]</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>[-114475975979.16652]</td>\n",
       "      <td>[-0.0003336223407074401]</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-6.637552e-05</td>\n",
       "      <td>[-114475975979.16682]</td>\n",
       "      <td>[-0.000669384695051789]</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.831800e-03</td>\n",
       "      <td>[-131053799921.13013]</td>\n",
       "      <td>[0.0020268148041209244]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>[-350720000048.40594]</td>\n",
       "      <td>[-0.0006466269207354713]</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.049032e-03</td>\n",
       "      <td>[-2085445059961.7305]</td>\n",
       "      <td>[0.00809900633159899]</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>[-2085445059961.7354]</td>\n",
       "      <td>[0.0048531514792076735]</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-1.712336e-02</td>\n",
       "      <td>[-2085445059961.7515]</td>\n",
       "      <td>[-0.012952157810806941]</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      coef_lasso                coef_lr                 coef_ridge  columns\n",
       "3   4.566514e-03    [639931397530.8513]     [0.007180167919224847]        3\n",
       "1  -1.618593e-04    [639931397530.8481]    [0.0021253246144553456]        1\n",
       "2   0.000000e+00    [639931397530.8474]    [0.0024781905960632317]        2\n",
       "0  -1.484594e-02    [639931397530.8342]    [-0.011783683129745481]        0\n",
       "12  5.481652e-04     [287172497520.279]    [0.0017179804979826074]       12\n",
       "6  -8.586456e-04   [287172497520.27783]    [-0.000337792614597654]        6\n",
       "5  -8.384169e-18   [270594673578.30835]   [-0.0020268148041210354]        5\n",
       "14  0.000000e+00   [50928473451.038734]    [0.0006466269207348052]       14\n",
       "18  6.478781e-02    [0.082733154296875]      [0.07591525042814762]       18\n",
       "19  0.000000e+00   [0.0220794677734375]     [0.013604860893836501]       19\n",
       "20 -2.413996e-02  [-0.0185089111328125]     [-0.02220821146650221]       20\n",
       "11  3.418903e-04   [-114475975979.1651]   [0.00022442207894068922]       11\n",
       "8   0.000000e+00  [-114475975979.16565]  [-0.00021932587930195702]        8\n",
       "7  -0.000000e+00   [-114475975979.1665]  [-0.00038227704726501166]        7\n",
       "10 -0.000000e+00  [-114475975979.16652]   [-0.0003336223407074401]       10\n",
       "9  -6.637552e-05  [-114475975979.16682]    [-0.000669384695051789]        9\n",
       "4   3.831800e-03  [-131053799921.13013]    [0.0020268148041209244]        4\n",
       "13 -0.000000e+00  [-350720000048.40594]   [-0.0006466269207354713]       13\n",
       "15  3.049032e-03  [-2085445059961.7305]      [0.00809900633159899]       15\n",
       "16 -0.000000e+00  [-2085445059961.7354]    [0.0048531514792076735]       16\n",
       "17 -1.712336e-02  [-2085445059961.7515]    [-0.012952157810806941]       17"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(range(xTrain.shape[1])), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lSEuSVChDWpKkQhnSkiQVypCWJKlQ23TFMUnaVEdHfdtJ+iNH0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCuUpWFKL8ZQpafvhSFqSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQg1udgGSytTR0ewKJDmSliSpUIa0JEmFMqQlSSrUVkM6IoZGxAMR8bOIeCQiPldNnxAR90fE4oiYExE7D3y5kiS1jt6MpP8AvDszDwEmAcdFxBTgcuBLmbkv8Dvg7IErU5Kk1rPVkM6aNdXLtuongXcDt1bTZwMnDUiFkiS1qF7tk46IQRGxEHgKuAv4FfBsZq6rmiwDxvbw2fMiYn5EzF+1alU9apYkqSX0KqQzc31mTgLGAYcD+3fXrIfPXpuZ7ZnZPmrUqL5XKklSi9mmo7sz81lgHjAF2C0iOi+GMg74TX1LkySptfXm6O5REbFb9fw1wHuARcDdwPuqZrOA2waqSEmSWlFvLgs6BpgdEYOohfrNmfm9iPgFcFNEXAb8FPj6ANYpSVLL2WpIZ+ZDwORupj9Obf+0JEkaAF5xTJKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklSo3lxxTFLhOjqaXYGkgeBIWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQg5tdgNSKOjqaXYGk7YEjaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQ3gVLUkNsy52/vEuYVONIWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFWqrIR0Rr4+IuyNiUUQ8EhEXVtNHRMRdEbG4etx94MuVJKl19GYkvQ74H5m5PzAF+FBETAQ+DczNzH2BudVrSZJUJ1sN6cxckZkPVs9fABYBY4GZwOyq2WzgpIEqUpKkVrRNd8GKiPHAZOB+YM/MXAG1II+I0T185jzgPIA3vOEN/alVUovo7V2wvFuWdnS9PnAsIoYB/wp8JDOf7+3nMvPazGzPzPZRo0b1pUZJklpSr0I6ItqoBfSNmflv1eSVETGmen8M8NTAlChJUmvqzdHdAXwdWJSZX+zy1u3ArOr5LOC2+pcnSVLr6s0+6bcDpwM/j4iF1bTPAF8Abo6Is4H/B/z5wJQoSVJr2mpIZ+a9QPTw9vT6liNJkjp5xTFJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhRrc7AKkHUlHR7MrkLQjcSQtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCDW52AZLUVx0d9W0nlcaRtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQnkKlnY4npYjaUfhSFqSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqE8BUsty1O1JJXOkbQkSYUypCVJKpQhLUlSobYa0hHxjYh4KiIe7jJtRETcFRGLq8fdB7ZMSZJaT29G0tcBx20y7dPA3MzcF5hbvZYkSXW01ZDOzHuAZzaZPBOYXT2fDZxU57okSWp5fd0nvWdmrgCoHkfXryRJkgQNOHAsIs6LiPkRMX/VqlUDvThJknYYfQ3plRExBqB6fKqnhpl5bWa2Z2b7qFGj+rg4SZJaT19D+nZgVvV8FnBbfcqRJEmdenMK1neAHwNviYhlEXE28AXg6IhYDBxdvZYkSXW01Wt3Z+YHenhrep1rkSRJXXjFMUmSCmVIS5JUKG9VKUmVbbktqbcwVSM4kpYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIK5V2wtN1o1l2HvNuRpGZxJC1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCegiVph+dpdNpeOZKWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoT8HSNp2e4qksktQ4jqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKU7C0TXp7Cla920mlqfe/Xf8vqDuOpCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFWq7PgWrVe/etD30ZXuoUZJK50hakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhtutTsJppIE4x8rQlSVJXjqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBWqZU7B6u3pTZ4GJakZBuKufs1qt61tm2V7yAVH0pIkFcqQliSpUIa0JEmF6ldIR8RxEfFYRCyJiE/XqyhJktSPkI6IQcA1wHuBicAHImJivQqTJKnV9WckfTiwJDMfz8yXgZuAmfUpS5IkRWb27YMR7wOOy8xzqtenA0dk5oc3aXcecF718i3AY30vd8DsATzd7CKaqNX7D64D+9/a/QfXQSP7/8bMHNWbhv05Tzq6mbZZ4mfmtcC1/VjOgIuI+ZnZ3uw6mqXV+w+uA/vf2v0H10Gp/e/P5u5lwOu7vB4H/KZ/5UiSpE79Cen/AvaNiAkRsTNwCnB7fcqSJEl93tydmesi4sPAD4BBwDcy85G6VdZYRW+Ob4BW7z+4Duy/Wn0dFNn/Ph84JkmSBpZXHJMkqVCGtCRJhWqZkI6IERFxV0Qsrh5376HdrKrN4oiY1WX6/46In0XEIxHxz9UV17Yb/el/RLw2Iu6IiEer/n+hsdX3Xx2+/89HxK8jYk3jqq6PrV2+NyKGRMSc6v37I2J8l/cuqqY/FhHHNrLueulr/yNiZETcHRFrIuLqRtddL/3o/9ERsSAifl49vrvRtddLP9bB4RGxsPr5WUT8aaNrJzNb4gf4B+DT1fNPA5d302YE8Hj1uHv1fPfqvV2rxwD+FTil2X1qVP+B1wLvqtrsDPwIeG+z+9Tg738KMAZY0+y+bGO/BwG/AvapvrufARM3aXMB8M/V81OAOdXziVX7IcCEaj6Dmt2nBvZ/F+AdwF8CVze7L03o/2Rg7+r5gcDyZvenCevgtcDg6vkY4KnO1436aZmRNLVLls6uns8GTuqmzbHAXZn5TGb+DrgLOA4gM5+v2gym9kVvb0fc9bn/mfliZt4NkLVLwD5I7bz47Ul/v/+fZOaKhlRaX725fG/XdXMrMD0iopp+U2b+ITOfAJZU89ue9Ln/mbk2M+8FXmpcuXXXn/7/NDM7r33xCDA0IoY0pOr66s86eDEz11XTh9KE3/utFNJ7dv6SrR5Hd9NmLPDrLq+XVdMAiIgfUPtL6gVqX+T2pN/9B4iI3YATgLkDVOdAqUv/t0O96dOGNtUvpOeAkb38bOn60/8dQb36/2fATzPzDwNU50Dq1zqIiCMi4hHg58BfdgnthujPZUGLExH/B9irm7cu7u0supm24S+nzDw2IoYCNwLvpjbSKsZA9z8iBgPfAa7KzMe3vcKBNdD93071pk89tdkR1kd/+r8j6Hf/I+IA4HLgmDrW1Uj9WgeZeT9wQETsD8yOiO9nZsO2ruxQIZ2Z7+npvYhYGRFjMnNFRHTuW9jUMmBal9fjgHmbLOOliLid2uaRokK6Af2/FlicmV+uQ7l114jvfzvUm8v3drZZVv0hNhx4ppefLV1/+r8j6Ff/I2Ic8F3gjMz81cCXOyDq8m8gMxdFxFpq++fnD1y5G2ulzd23A51H684CbuumzQ+AYyJi9+ro32OAH0TEsOoXe+do8k+ARxtQcz31uf8AEXEZtX+4H2lArQOhX/3fjvXm8r1d1837gB9m7UiZ24FTqiNfJwD7Ag80qO566U//dwR97n+1a+sO4KLMvK9hFddff9bBhOp3PhHxRmp3cnyyMWVXmn3kXaN+qO1fmAssrh5HVNPbga91aXcWtQNklgD/vZq2J7Uv+iFqB1D8Iw0+wq/J/R9HbdPPImBh9XNOs/vUqP5X0/+B2l/br1aPHc3u0zb0/U+AX1I7wvXiatqlwInV86HALVWfHwD26fLZi6vPPcZ2dkR/nfr/JLUR1Zrqe5/Y6Pqb1X/gr4G1Xf7PLwRGN7s/DV4Hp1e/8xdSO2D2pEbX7mVBJUkqVCtt7pYkabtiSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKtT/B5lTG45XIAvZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe6196d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ll =y_test_pred_lasso.reshape(-1,1)\n",
    "\n",
    "\n",
    "#结果和LR模型相比，得分很接近#在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(yTest - ll,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Histogram of Residuals\") \n",
    "ax.legend(loc='best');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe62f940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#还可以观察预测值与真值的散点图\n",
    "plt.figure(figsize=(7, 5))\n",
    "plt.scatter(yTest, ll)\n",
    "plt.plot([-0.5, 0.5], [-0.5, 0.5], '--k')   #数据已经归一，0.5倍标准差即可\n",
    "plt.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
